Overview

Dataset statistics

Number of variables30
Number of observations100000
Missing cells222977
Missing cells (%)7.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory22.9 MiB
Average record size in memory240.0 B

Variable types

CAT21
NUM6
BOOL2
DATE1

Warnings

crash_time has a high cardinality: 1440 distinct values High cardinality
zip_code has a high cardinality: 203 distinct values High cardinality
latitude has a high cardinality: 33676 distinct values High cardinality
longitude has a high cardinality: 26495 distinct values High cardinality
location has a high cardinality: 44606 distinct values High cardinality
on_street_name has a high cardinality: 4327 distinct values High cardinality
off_street_name has a high cardinality: 4897 distinct values High cardinality
cross_street_name has a high cardinality: 22829 distinct values High cardinality
nearest_street has a high cardinality: 27727 distinct values High cardinality
combine_location has a high cardinality: 44606 distinct values High cardinality
number_of_motorist_injured is highly correlated with number_of_persons_injuredHigh correlation
number_of_persons_injured is highly correlated with number_of_motorist_injuredHigh correlation
crash_year is highly correlated with collision_idHigh correlation
collision_id is highly correlated with crash_yearHigh correlation
borough has 35026 (35.0%) missing values Missing
zip_code has 35034 (35.0%) missing values Missing
on_street_name has 26009 (26.0%) missing values Missing
off_street_name has 52875 (52.9%) missing values Missing
cross_street_name has 74033 (74.0%) missing values Missing
cross_street_name is uniformly distributed Uniform
collision_id has unique values Unique
number_of_persons_injured has 72699 (72.7%) zeros Zeros
number_of_pedestrians_injured has 95454 (95.5%) zeros Zeros
number_of_motorist_injured has 81887 (81.9%) zeros Zeros

Reproduction

Analysis started2020-12-12 13:39:51.104124
Analysis finished2020-12-12 13:40:19.028106
Duration27.92 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

Distinct551
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size781.2 KiB
Minimum2013-03-23 00:00:00
Maximum2020-09-29 00:00:00
2020-12-12T14:40:19.141803image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:40:19.327435image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

crash_time
Categorical

HIGH CARDINALITY

Distinct1440
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size781.2 KiB
0:00
 
1637
17:00
 
1363
16:00
 
1360
14:00
 
1298
15:00
 
1246
Other values (1435)
93096 
ValueCountFrequency (%) 
0:0016371.6%
 
17:0013631.4%
 
16:0013601.4%
 
14:0012981.3%
 
15:0012461.2%
 
18:0012311.2%
 
13:0011531.2%
 
12:0011031.1%
 
19:009961.0%
 
10:009711.0%
 
Other values (1430)8764287.6%
 
2020-12-12T14:40:19.666538image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T14:40:19.810585image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length5
Median length5
Mean length4.74399
Min length4

borough
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing35026
Missing (%)35.0%
Memory size781.2 KiB
BROOKLYN
22118 
QUEENS
18322 
BRONX
11927 
MANHATTAN
10637 
STATEN ISLAND
 
1970
ValueCountFrequency (%) 
BROOKLYN2211822.1%
 
QUEENS1832218.3%
 
BRONX1192711.9%
 
MANHATTAN1063710.6%
 
STATEN ISLAND19702.0%
 
(Missing)3502635.0%
 
2020-12-12T14:40:20.008231image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T14:40:20.354500image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:40:20.532015image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length13
Median length6
Mean length5.72932
Min length3

zip_code
Categorical

HIGH CARDINALITY
MISSING

Distinct203
Distinct (%)0.3%
Missing35034
Missing (%)35.0%
Memory size781.2 KiB
11207
 
1510
11236
 
1175
11212
 
1088
11208
 
1071
11385
 
1015
Other values (198)
59107 
ValueCountFrequency (%) 
1120715101.5%
 
1123611751.2%
 
1121210881.1%
 
1120810711.1%
 
1138510151.0%
 
112039881.0%
 
114349601.0%
 
112349310.9%
 
112269230.9%
 
113688640.9%
 
Other values (193)5444154.4%
 
(Missing)3503435.0%
 
2020-12-12T14:40:20.689205image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique12 ?
Unique (%)< 0.1%
2020-12-12T14:40:20.849552image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length5
Median length5
Mean length4.29932
Min length3

latitude
Categorical

HIGH CARDINALITY

Distinct33676
Distinct (%)33.7%
Missing0
Missing (%)0.0%
Memory size781.2 KiB
nan
 
8035
0.0
 
169
40.861862
 
79
40.8047
 
56
40.820305
 
52
Other values (33671)
91609 
ValueCountFrequency (%) 
nan80358.0%
 
0.01690.2%
 
40.861862790.1%
 
40.8047560.1%
 
40.820305520.1%
 
40.67573499999999648< 0.1%
 
40.69603348< 0.1%
 
40.65857747< 0.1%
 
40.73778499999999545< 0.1%
 
40.65186343< 0.1%
 
Other values (33666)9137891.4%
 
2020-12-12T14:40:21.170028image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique16219 ?
Unique (%)16.2%
2020-12-12T14:40:21.338919image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length18
Median length9
Mean length9.84755
Min length3

longitude
Categorical

HIGH CARDINALITY

Distinct26495
Distinct (%)26.5%
Missing0
Missing (%)0.0%
Memory size781.2 KiB
nan
 
8035
0.0
 
169
-73.91282
 
83
-73.89063
 
73
-73.91243
 
61
Other values (26490)
91579 
ValueCountFrequency (%) 
nan80358.0%
 
0.01690.2%
 
-73.91282830.1%
 
-73.89063730.1%
 
-73.91243610.1%
 
-73.89083000000001580.1%
 
-73.89686530.1%
 
-73.98453520.1%
 
-73.9375547< 0.1%
 
-73.9619146< 0.1%
 
Other values (26485)9132391.3%
 
2020-12-12T14:40:21.607304image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique10002 ?
Unique (%)10.0%
2020-12-12T14:40:21.759293image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length18
Median length9
Mean length10.21951
Min length3

location
Categorical

HIGH CARDINALITY

Distinct44606
Distinct (%)44.6%
Missing0
Missing (%)0.0%
Memory size781.2 KiB
(nannan)
 
8035
(0.0, 0.0)
 
169
(40.861862, -73.91282)
 
79
(40.8047, -73.91243)
 
55
(40.820305, -73.89083)
 
52
Other values (44601)
91610 
ValueCountFrequency (%) 
(nannan)80358.0%
 
(0.0, 0.0)1690.2%
 
(40.861862, -73.91282)790.1%
 
(40.8047, -73.91243)550.1%
 
(40.820305, -73.89083)520.1%
 
(40.675735, -73.89686)48< 0.1%
 
(40.696033, -73.98453)48< 0.1%
 
(40.658577, -73.89063)47< 0.1%
 
(40.737785, -73.93496)43< 0.1%
 
(40.733536, -73.87035)41< 0.1%
 
Other values (44596)9138391.4%
 
2020-12-12T14:40:22.144809image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique29003 ?
Unique (%)29.0%
2020-12-12T14:40:22.302042image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length25
Median length22
Mean length20.60857
Min length8

on_street_name
Categorical

HIGH CARDINALITY
MISSING

Distinct4327
Distinct (%)5.8%
Missing26009
Missing (%)26.0%
Memory size781.2 KiB
BELT PARKWAY
 
1616
LONG ISLAND EXPRESSWAY
 
1053
BROOKLYN QUEENS EXPRESSWAY
 
956
BROADWAY
 
863
FDR DRIVE
 
852
Other values (4322)
68651 
ValueCountFrequency (%) 
BELT PARKWAY 16161.6%
 
LONG ISLAND EXPRESSWAY 10531.1%
 
BROOKLYN QUEENS EXPRESSWAY 9561.0%
 
BROADWAY 8630.9%
 
FDR DRIVE 8520.9%
 
GRAND CENTRAL PKWY 8200.8%
 
ATLANTIC AVENUE 7170.7%
 
MAJOR DEEGAN EXPRESSWAY 6740.7%
 
CROSS BRONX EXPY 6520.7%
 
CROSS ISLAND PARKWAY 6050.6%
 
Other values (4317)6518365.2%
 
(Missing)2600926.0%
 
2020-12-12T14:40:22.524204image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1369 ?
Unique (%)1.9%
2020-12-12T14:40:22.686739image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length32
Median length32
Mean length24.45739
Min length3

off_street_name
Categorical

HIGH CARDINALITY
MISSING

Distinct4897
Distinct (%)10.4%
Missing52875
Missing (%)52.9%
Memory size781.2 KiB
3 AVENUE
 
432
BROADWAY
 
424
2 AVENUE
 
340
LINDEN BOULEVARD
 
280
5 AVENUE
 
247
Other values (4892)
45402 
ValueCountFrequency (%) 
3 AVENUE4320.4%
 
BROADWAY4240.4%
 
2 AVENUE3400.3%
 
LINDEN BOULEVARD2800.3%
 
5 AVENUE2470.2%
 
ATLANTIC AVENUE2400.2%
 
1 AVENUE2370.2%
 
7 AVENUE2290.2%
 
PARK AVENUE2220.2%
 
QUEENS BOULEVARD2180.2%
 
Other values (4887)4425644.3%
 
(Missing)5287552.9%
 
2020-12-12T14:40:22.858813image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1704 ?
Unique (%)3.6%
2020-12-12T14:40:23.068151image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length32
Median length3
Mean length7.80378
Min length1

cross_street_name
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct22829
Distinct (%)87.9%
Missing74033
Missing (%)74.0%
Memory size781.2 KiB
772 EDGEWATER ROAD
 
35
501 GATEWAY DRIVE
 
21
90-15 QUEENS BOULEVARD
 
19
123-01 ROOSEVELT AVENUE
 
18
2100 BARTOW AVENUE
 
14
Other values (22824)
25860 
ValueCountFrequency (%) 
772 EDGEWATER ROAD 35< 0.1%
 
501 GATEWAY DRIVE 21< 0.1%
 
90-15 QUEENS BOULEVARD 19< 0.1%
 
123-01 ROOSEVELT AVENUE 18< 0.1%
 
2100 BARTOW AVENUE 14< 0.1%
 
985 RICHMOND AVENUE 13< 0.1%
 
135-05 20 AVENUE 12< 0.1%
 
355 FOOD CENTER DRIVE 12< 0.1%
 
1 ORCHARD BEACH ROAD 12< 0.1%
 
815 HUTCHINSON RIVER PARKWAY 12< 0.1%
 
Other values (22819)2579925.8%
 
(Missing)7403374.0%
 
2020-12-12T14:40:23.282913image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique20794 ?
Unique (%)80.1%
2020-12-12T14:40:23.480722image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length40
Median length3
Mean length12.60779
Min length3

number_of_persons_injured
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.37196
Minimum0
Maximum15
Zeros72699
Zeros (%)72.7%
Memory size781.2 KiB
2020-12-12T14:40:23.591043image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum15
Range15
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.7439161865
Coefficient of variation (CV)1.999989748
Kurtosis16.5808926
Mean0.37196
Median Absolute Deviation (MAD)0
Skewness3.147118256
Sum37196
Variance0.5534112925
MonotocityNot monotonic
2020-12-12T14:40:23.689600image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%) 
07269972.7%
 
12101121.0%
 
241254.1%
 
313081.3%
 
45230.5%
 
51960.2%
 
6770.1%
 
736< 0.1%
 
814< 0.1%
 
95< 0.1%
 
Other values (3)6< 0.1%
 
ValueCountFrequency (%) 
07269972.7%
 
12101121.0%
 
241254.1%
 
313081.3%
 
45230.5%
 
ValueCountFrequency (%) 
151< 0.1%
 
113< 0.1%
 
102< 0.1%
 
95< 0.1%
 
814< 0.1%
 
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.2 KiB
0
99816 
1
 
176
2
 
7
3
 
1
ValueCountFrequency (%) 
09981699.8%
 
11760.2%
 
27< 0.1%
 
31< 0.1%
 
2020-12-12T14:40:23.809449image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)< 0.1%
2020-12-12T14:40:23.945205image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:40:24.042122image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

number_of_pedestrians_injured
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.04739
Minimum0
Maximum6
Zeros95454
Zeros (%)95.5%
Memory size781.2 KiB
2020-12-12T14:40:24.136529image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2234383296
Coefficient of variation (CV)4.714883512
Kurtosis38.41899762
Mean0.04739
Median Absolute Deviation (MAD)0
Skewness5.270026474
Sum4739
Variance0.04992468715
MonotocityNot monotonic
2020-12-12T14:40:24.229102image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%) 
09545495.5%
 
143834.4%
 
21420.1%
 
317< 0.1%
 
62< 0.1%
 
51< 0.1%
 
41< 0.1%
 
ValueCountFrequency (%) 
09545495.5%
 
143834.4%
 
21420.1%
 
317< 0.1%
 
41< 0.1%
 
ValueCountFrequency (%) 
62< 0.1%
 
51< 0.1%
 
41< 0.1%
 
317< 0.1%
 
21420.1%
 
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.2 KiB
0
99936 
1
 
64
ValueCountFrequency (%) 
09993699.9%
 
1640.1%
 
2020-12-12T14:40:24.307501image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.2 KiB
0
95147 
1
 
4744
2
 
107
3
 
2
ValueCountFrequency (%) 
09514795.1%
 
147444.7%
 
21070.1%
 
32< 0.1%
 
2020-12-12T14:40:24.593572image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T14:40:24.670935image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:40:24.763576image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.2 KiB
0
99975 
1
 
25
ValueCountFrequency (%) 
099975> 99.9%
 
125< 0.1%
 
2020-12-12T14:40:24.834381image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

number_of_motorist_injured
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.27492
Minimum0
Maximum15
Zeros81887
Zeros (%)81.9%
Memory size781.2 KiB
2020-12-12T14:40:24.977752image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum15
Range15
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.711058401
Coefficient of variation (CV)2.586419326
Kurtosis21.76181987
Mean0.27492
Median Absolute Deviation (MAD)0
Skewness3.819224309
Sum27492
Variance0.5056040496
MonotocityNot monotonic
2020-12-12T14:40:25.077131image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%) 
08188781.9%
 
11224312.2%
 
237673.8%
 
312591.3%
 
45230.5%
 
51890.2%
 
6730.1%
 
734< 0.1%
 
814< 0.1%
 
95< 0.1%
 
Other values (3)6< 0.1%
 
ValueCountFrequency (%) 
08188781.9%
 
11224312.2%
 
237673.8%
 
312591.3%
 
45230.5%
 
ValueCountFrequency (%) 
151< 0.1%
 
113< 0.1%
 
102< 0.1%
 
95< 0.1%
 
814< 0.1%
 
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.2 KiB
0
99904 
1
 
89
2
 
6
3
 
1
ValueCountFrequency (%) 
09990499.9%
 
1890.1%
 
26< 0.1%
 
31< 0.1%
 
2020-12-12T14:40:25.292900image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)< 0.1%
2020-12-12T14:40:25.375146image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:40:25.515456image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1
Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.2 KiB
Driver Inattention/Distraction
25605 
Unspecified
25253 
Following Too Closely
7530 
Other factor
6994 
Failure to Yield Right-of-Way
6023 
Other values (11)
28595 
ValueCountFrequency (%) 
Driver Inattention/Distraction2560525.6%
 
Unspecified2525325.3%
 
Following Too Closely75307.5%
 
Other factor69947.0%
 
Failure to Yield Right-of-Way60236.0%
 
Backing Unsafely40334.0%
 
Passing or Lane Usage Improper39794.0%
 
Passing Too Closely36763.7%
 
Other Vehicular30713.1%
 
Unsafe Lane Changing25882.6%
 
Other values (6)1124811.2%
 
2020-12-12T14:40:25.632128image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T14:40:25.760899image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length30
Median length19
Mean length20.43398
Min length11
Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.2 KiB
Unspecified
67739 
Other factor
19861 
Driver Inattention/Distraction
 
5284
Following Too Closely
 
1296
Other Vehicular
 
1249
Other values (12)
 
4571
ValueCountFrequency (%) 
Unspecified6773967.7%
 
Other factor1986119.9%
 
Driver Inattention/Distraction52845.3%
 
Following Too Closely12961.3%
 
Other Vehicular12491.2%
 
Passing or Lane Usage Improper8020.8%
 
Failure to Yield Right-of-Way7160.7%
 
Passing Too Closely5380.5%
 
Unsafe Lane Changing4020.4%
 
Unsafe Speed3830.4%
 
Other values (7)17301.7%
 
2020-12-12T14:40:25.960295image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T14:40:26.089481image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length53
Median length11
Mean length13.00979
Min length11
Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.2 KiB
Other factor
91338 
Unspecified
 
8197
Following Too Closely
 
176
Other Vehicular
 
171
Driver Inattention/Distraction
 
118
ValueCountFrequency (%) 
Other factor9133891.3%
 
Unspecified81978.2%
 
Following Too Closely1760.2%
 
Other Vehicular1710.2%
 
Driver Inattention/Distraction1180.1%
 
2020-12-12T14:40:26.204588image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T14:40:26.287370image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:40:26.447801image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length30
Median length12
Mean length11.96024
Min length11

collision_id
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct100000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4226109.341
Minimum2568
Maximum4353706
Zeros0
Zeros (%)0.0%
Memory size781.2 KiB
2020-12-12T14:40:26.625648image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum2568
5-th percentile3665427.95
Q14182342.75
median4300224
Q34328315.25
95-th percentile4348345.05
Maximum4353706
Range4351138
Interquartile range (IQR)145972.5

Descriptive statistics

Standard deviation165356.0511
Coefficient of variation (CV)0.03912725341
Kurtosis45.22161792
Mean4226109.341
Median Absolute Deviation (MAD)51882.5
Skewness-3.965406795
Sum4.226109341e+11
Variance2.734262364e+10
MonotocityNot monotonic
2020-12-12T14:40:26.764617image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
41963511< 0.1%
 
41968841< 0.1%
 
43039711< 0.1%
 
43357981< 0.1%
 
41599941< 0.1%
 
43219181< 0.1%
 
41678761< 0.1%
 
43108321< 0.1%
 
43187271< 0.1%
 
43191721< 0.1%
 
Other values (99990)99990> 99.9%
 
ValueCountFrequency (%) 
25681< 0.1%
 
690101< 0.1%
 
742941< 0.1%
 
1277331< 0.1%
 
2105911< 0.1%
 
ValueCountFrequency (%) 
43537061< 0.1%
 
43537051< 0.1%
 
43537011< 0.1%
 
43536721< 0.1%
 
43536631< 0.1%
 
Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.2 KiB
Sedan
46790 
Station Wagon/Sport Utility Vehicle
35766 
Other factor
6477 
Taxi
 
3478
Pick-up Truck
 
2615
Other values (4)
4874 
ValueCountFrequency (%) 
Sedan4679046.8%
 
Station Wagon/Sport Utility Vehicle3576635.8%
 
Other factor64776.5%
 
Taxi34783.5%
 
Pick-up Truck26152.6%
 
Box Truck19461.9%
 
Bike14371.4%
 
Tractor Truck Diesel7510.8%
 
unspecified7400.7%
 
2020-12-12T14:40:26.966288image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T14:40:27.052244image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:40:27.209631image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length35
Median length5
Mean length16.57813
Min length4
Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.2 KiB
Sedan
31369 
unspecified
26589 
Station Wagon/Sport Utility Vehicle
24773 
Other factor
4450 
Bike
3586 
Other values (6)
9233 
ValueCountFrequency (%) 
Sedan3136931.4%
 
unspecified2658926.6%
 
Station Wagon/Sport Utility Vehicle2477324.8%
 
Other factor44504.5%
 
Bike35863.6%
 
Taxi23002.3%
 
Pick-up Truck22822.3%
 
Box Truck21462.1%
 
Bus10111.0%
 
Tractor Truck Diesel7630.8%
 
2020-12-12T14:40:27.334247image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T14:40:27.511716image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length35
Median length11
Mean length14.67906
Min length3
Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.2 KiB
Other factor
92036 
Sedan
 
4129
Station Wagon/Sport Utility Vehicle
 
3380
Pick-up Truck
 
195
Taxi
 
187
ValueCountFrequency (%) 
Other factor9203692.0%
 
Sedan41294.1%
 
Station Wagon/Sport Utility Vehicle33803.4%
 
Pick-up Truck1950.2%
 
Taxi1870.2%
 
Box Truck730.1%
 
2020-12-12T14:40:27.631847image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T14:40:27.707832image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:40:27.832553image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length35
Median length12
Mean length12.47317
Min length4

crash_day
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.2 KiB
Friday
15494 
Tuesday
14653 
Thursday
14573 
Wednesday
14285 
Monday
14242 
Other values (2)
26753 
ValueCountFrequency (%) 
Friday1549415.5%
 
Tuesday1465314.7%
 
Thursday1457314.6%
 
Wednesday1428514.3%
 
Monday1424214.2%
 
Saturday1396414.0%
 
Sunday1278912.8%
 
2020-12-12T14:40:28.016904image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T14:40:28.101743image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:40:28.242434image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length9
Median length7
Mean length7.14582
Min length6

crash_month
Real number (ℝ≥0)

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.02299
Minimum2
Maximum12
Zeros0
Zeros (%)0.0%
Memory size781.2 KiB
2020-12-12T14:40:28.356748image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q16
median7
Q38
95-th percentile9
Maximum12
Range10
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.828325706
Coefficient of variation (CV)0.2603343741
Kurtosis0.02879865261
Mean7.02299
Median Absolute Deviation (MAD)1
Skewness-0.2523065626
Sum702299
Variance3.342774888
MonotocityNot monotonic
2020-12-12T14:40:28.511458image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%) 
82695027.0%
 
72358923.6%
 
91322613.2%
 
61164611.6%
 
584268.4%
 
476797.7%
 
341334.1%
 
1136033.6%
 
125060.5%
 
101540.2%
 
ValueCountFrequency (%) 
2880.1%
 
341334.1%
 
476797.7%
 
584268.4%
 
61164611.6%
 
ValueCountFrequency (%) 
125060.5%
 
1136033.6%
 
101540.2%
 
91322613.2%
 
82695027.0%
 

crash_year
Real number (ℝ≥0)

HIGH CORRELATION

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019.37856
Minimum2013
Maximum2020
Zeros0
Zeros (%)0.0%
Memory size781.2 KiB
2020-12-12T14:40:28.614584image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum2013
5-th percentile2017
Q12019
median2020
Q32020
95-th percentile2020
Maximum2020
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.7793320217
Coefficient of variation (CV)0.0003859266594
Kurtosis3.639582223
Mean2019.37856
Median Absolute Deviation (MAD)0
Skewness-1.657671392
Sum201937856
Variance0.6073584
MonotocityNot monotonic
2020-12-12T14:40:28.717272image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%) 
20205003750.0%
 
20194388143.9%
 
201758715.9%
 
20181480.1%
 
201543< 0.1%
 
201319< 0.1%
 
20141< 0.1%
 
ValueCountFrequency (%) 
201319< 0.1%
 
20141< 0.1%
 
201543< 0.1%
 
201758715.9%
 
20181480.1%
 
ValueCountFrequency (%) 
20205003750.0%
 
20194388143.9%
 
20181480.1%
 
201758715.9%
 
201543< 0.1%
 

nearest_street
Categorical

HIGH CARDINALITY

Distinct27727
Distinct (%)27.7%
Missing0
Missing (%)0.0%
Memory size781.2 KiB
nan
26908 
3 AVENUE
 
432
BROADWAY
 
424
2 AVENUE
 
340
LINDEN BOULEVARD
 
280
Other values (27722)
71616 
ValueCountFrequency (%) 
nan2690826.9%
 
3 AVENUE4320.4%
 
BROADWAY4240.4%
 
2 AVENUE3400.3%
 
LINDEN BOULEVARD2800.3%
 
5 AVENUE2470.2%
 
ATLANTIC AVENUE2400.2%
 
1 AVENUE2370.2%
 
7 AVENUE2290.2%
 
PARK AVENUE2220.2%
 
Other values (27717)7044170.4%
 
2020-12-12T14:40:29.002682image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique22498 ?
Unique (%)22.5%
2020-12-12T14:40:29.173375image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length40
Median length13
Mean length17.41157
Min length1

combine_location
Categorical

HIGH CARDINALITY

Distinct44606
Distinct (%)44.6%
Missing0
Missing (%)0.0%
Memory size781.2 KiB
(nannan)
 
8035
(0.00.0)
 
169
(40.861862-73.91282)
 
79
(40.8047-73.91243)
 
55
(40.820305-73.89083000000001)
 
52
Other values (44601)
91610 
ValueCountFrequency (%) 
(nannan)80358.0%
 
(0.00.0)1690.2%
 
(40.861862-73.91282)790.1%
 
(40.8047-73.91243)550.1%
 
(40.820305-73.89083000000001)520.1%
 
(40.696033-73.98453)48< 0.1%
 
(40.675734999999996-73.89686)48< 0.1%
 
(40.658577-73.89063)47< 0.1%
 
(40.737784999999995-73.93496)43< 0.1%
 
(40.733536-73.87035)41< 0.1%
 
Other values (44596)9138391.4%
 
2020-12-12T14:40:29.519312image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique29003 ?
Unique (%)29.0%
2020-12-12T14:40:29.671871image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length38
Median length20
Mean length22.06706
Min length8

Interactions

2020-12-12T14:40:07.246229image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:40:07.479427image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:40:07.640202image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:40:07.792300image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:40:08.001959image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:40:08.145984image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:40:08.300795image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:40:08.496081image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:40:08.641388image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:40:08.790056image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:40:08.987584image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:40:09.126759image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:40:09.278382image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:40:09.468306image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:40:09.618897image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:40:09.770048image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:40:09.967235image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:40:10.211138image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:40:10.370395image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:40:10.557683image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:40:10.696525image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:40:10.837623image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:40:11.231415image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:40:11.572674image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:40:11.717495image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:40:11.875134image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:40:12.123319image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:40:12.520694image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:40:12.656575image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:40:12.791426image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:40:13.113218image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:40:13.273012image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:40:13.497969image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:40:13.867137image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:40:14.065935image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:40:14.211892image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Correlations

2020-12-12T14:40:29.804869image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-12-12T14:40:30.317483image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-12-12T14:40:30.597387image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-12-12T14:40:30.847142image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-12-12T14:40:31.275799image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-12-12T14:40:14.901893image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:40:16.828995image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:40:18.113494image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:40:18.502219image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Sample

First rows

crash_datecrash_timeboroughzip_codelatitudelongitudelocationon_street_nameoff_street_namecross_street_namenumber_of_persons_injurednumber_of_persons_killednumber_of_pedestrians_injurednumber_of_pedestrians_killednumber_of_cyclist_injurednumber_of_cyclist_killednumber_of_motorist_injurednumber_of_motorist_killedcontributing_factor_vehicle_1contributing_factor_vehicle_2contributing_factor_vehicle_3collision_idvehicle_type_code_1vehicle_type_code_2vehicle_type_code_3crash_daycrash_monthcrash_yearnearest_streetcombine_location
02017-04-1823:10STATEN ISLAND1031240.536728000000004-74.193344(40.536728, -74.193344)NaNNaN243 DARLINGTON AVENUE00000000Driver Inattention/DistractionUnspecifiedOther factor3654181Station Wagon/Sport Utility VehicleunspecifiedOther factorTuesday42017243 DARLINGTON AVENUE(40.536728000000004-74.193344)
12017-05-0613:00BRONX1047240.829052000000004-73.85038(40.829052, -73.85038)CASTLE HILL AVENUEBLACKROCK AVENUENaN10100000Failure to Yield Right-of-WayOther factorOther factor3665311SedanunspecifiedOther factorSaturday52017BLACKROCK AVENUE(40.829052000000004-73.85038)
22017-04-2717:15QUEENS1142040.677303-73.804565(40.677303, -73.804565)135 STREETFOCH BOULEVARDNaN00000000Driver Inattention/DistractionUnspecifiedOther factor3658491SedanSedanOther factorThursday42017FOCH BOULEVARD(40.677303-73.804565)
32017-05-0920:10NaNNaN40.624958-74.145775(40.624958, -74.145775)FOREST AVENUERICHMOND AVENUENaN10000010UnspecifiedUnspecifiedUnspecified3666554Other factorSedanOther factorTuesday52017RICHMOND AVENUE(40.624958-74.145775)
42017-04-1814:00BRONX1045640.828846-73.90312(40.828846, -73.90312)NaNNaN1167 BOSTON ROAD00000000Driver Inattention/DistractionUnspecifiedOther factor3653269SedanStation Wagon/Sport Utility VehicleOther factorTuesday420171167 BOSTON ROAD(40.828846-73.90312)
52017-05-0810:33NaNNaN40.556453999999995-74.20777(40.556454, -74.20777)WEST SHORE EXPRESSWAYNaNNaN00000000Unsafe Lane ChangingUnspecifiedOther factor3666365SedanSedanOther factorMonday52017nan(40.556453999999995-74.20777)
62017-05-106:10NaNNaN40.740025-73.97626(40.740025, -73.97626)1 AVENUEEAST 28 STREETNaN00000000Passing or Lane Usage ImproperUnspecifiedOther factor3666842TaxiBox TruckOther factorWednesday52017EAST 28 STREET(40.740025-73.97626)
72017-04-249:30BROOKLYN1120340.651646-73.93233000000001(40.651646, -73.93233)EAST 48 STREETCHURCH AVENUENaN00000000Other VehicularOther VehicularOther factor3657123Station Wagon/Sport Utility VehicleStation Wagon/Sport Utility VehicleOther factorMonday42017CHURCH AVENUE(40.651646-73.93233000000001)
82017-04-1413:00NaNNaN40.7518-73.817314(40.7518, -73.817314)ROBINSON STREETNaNNaN00000000Passing Too CloselyUnspecifiedOther factor3651039SedanStation Wagon/Sport Utility VehicleOther factorFriday42017nan(40.7518-73.817314)
92017-05-021:00BRONX1047440.816864-73.88274399999999(40.816864, -73.882744)NaNNaN772 EDGEWATER ROAD00000000UnspecifiedOther factorOther factor3661896Pick-up TruckunspecifiedOther factorTuesday52017772 EDGEWATER ROAD(40.816864-73.88274399999999)

Last rows

crash_datecrash_timeboroughzip_codelatitudelongitudelocationon_street_nameoff_street_namecross_street_namenumber_of_persons_injurednumber_of_persons_killednumber_of_pedestrians_injurednumber_of_pedestrians_killednumber_of_cyclist_injurednumber_of_cyclist_killednumber_of_motorist_injurednumber_of_motorist_killedcontributing_factor_vehicle_1contributing_factor_vehicle_2contributing_factor_vehicle_3collision_idvehicle_type_code_1vehicle_type_code_2vehicle_type_code_3crash_daycrash_monthcrash_yearnearest_streetcombine_location
999902019-11-0819:20BROOKLYN11218nannan(nannan)OCEAN PARKWAYAVENUE CNaN00000000UnspecifiedUnspecifiedOther factor4238828SedanSedanOther factorFriday112019AVENUE C(nannan)
999912019-11-1115:55NaNNaN40.66154-73.98274(40.66154, -73.98274)16 STREETNaNNaN00000000Other factorUnspecifiedOther factor4239244SedanStation Wagon/Sport Utility VehicleOther factorMonday112019nan(40.66154-73.98274)
999922019-11-1311:00BRONX1046140.836597-73.840546(40.836597, -73.840546)NaNNaN1332 COMMERCE AVENUE00000000UnspecifiedUnspecifiedOther factor4240499Pick-up TruckSedanOther factorWednesday1120191332 COMMERCE AVENUE(40.836597-73.840546)
999932019-12-047:00QUEENS1138540.703407-73.883484(40.703407, -73.883484)NaNNaN71-17 69 STREET00000000Passing Too CloselyUnspecifiedOther factor4252028Station Wagon/Sport Utility VehicleunspecifiedOther factorWednesday12201971-17 69 STREET(40.703407-73.883484)
999942019-11-1513:05BROOKLYN1120640.701862-73.94383(40.701862, -73.94383)WHIPPLE STREETBROADWAYNaN00000000Following Too CloselyUnspecifiedOther factor4242657SedanStation Wagon/Sport Utility VehicleOther factorFriday112019BROADWAY(40.701862-73.94383)
999952019-11-2015:00BROOKLYN1121040.618893-73.94641999999999(40.618893, -73.94642)NaNNaN1314 EAST 29 STREET00000000UnspecifiedOther factorOther factor4244961Station Wagon/Sport Utility VehicleunspecifiedOther factorWednesday1120191314 EAST 29 STREET(40.618893-73.94641999999999)
999962019-12-0111:22QUEENS1136740.72338-73.81475(40.72338, -73.81475)NaNNaN150-62 76 ROAD00000000UnspecifiedUnspecifiedOther factor4250093Station Wagon/Sport Utility VehicleStation Wagon/Sport Utility VehicleOther factorSunday122019150-62 76 ROAD(40.72338-73.81475)
999972019-11-2121:30BROOKLYN1124940.71082-73.96853(40.71082, -73.96853)BROADWAYKENT AVENUENaN00000000Passing Too CloselyUnspecifiedOther factor4245290SedanBox TruckOther factorThursday112019KENT AVENUE(40.71082-73.96853)
999982019-11-1817:28BROOKLYN1123440.63118-73.928185(40.63118, -73.928185)NaNNaN1695 UTICA AVENUE00000000Driver Inattention/DistractionUnspecifiedOther factor4243646SedanBusOther factorMonday1120191695 UTICA AVENUE(40.63118-73.928185)
999992019-11-1720:42MANHATTAN1001740.75076-73.96843(40.75076, -73.96843)EAST 45 STREET1 AVENUENaN00000000Driver Inattention/DistractionDriver Inattention/DistractionOther factor4247517SedanStation Wagon/Sport Utility VehicleOther factorSunday1120191 AVENUE(40.75076-73.96843)